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首页> 外文期刊>Journal of Seismic Exploration >THE FINITE DIFFERENCE CONTRAST SOURCE INVERSION WITH SUPER MEMORY HYBRID CONJUGATE GRADIENT METHOD
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THE FINITE DIFFERENCE CONTRAST SOURCE INVERSION WITH SUPER MEMORY HYBRID CONJUGATE GRADIENT METHOD

机译:超存储器混合共轭梯度法的有限差分对比度源反演

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摘要

The finite difference contrast source inversion (FDCSI) is an algorithm to solve the wave equation inverse scattering problem. This algorithm's forward operator is only related to the background medium, which does not change during the iterative optimization process. Therefore, an LU decomposition is required only once for the forward operator, which has lower computation cost. Because of finite difference operator, FDCSI can be applied to inhomogeneous background medium. FDCSI transforms the inverse scattering problem of wave equation into an optimization problem, which can be solved by conjugate gradient method. But conventional conjugate gradient method converges slowly, which affects computing efficiency, and the Newton method increases computation and memory. In order to improve the convergence speed for frequency domain acoustic equation, the super memory hybrid conjugate gradient method (SMHCG) is introduced into FDCSI. SMHCG is improved on the basis of the super memory gradient method to adapt to FDCSI. SMHCG accelerates the convergence of objective function without any increase computation and memory. The advantages of SMHCG had been verified on the Marmousi model.
机译:有限差分对比度源反演(FDCSI)是一种解决波动方程反散射问题的算法。该算法的前向运算符仅与背景介质有关,在迭代优化过程中不会改变。因此,前向运算符仅需要一次LU分解,因此计算成本较低。由于有限差分算子,FDCSI可以应用于不均匀的背景介质。 FDCSI将波动方程的逆散射问题转化为优化问题,可以通过共轭梯度法解决。但是传统的共轭梯度法收敛缓慢,影响了计算效率,牛顿法增加了计算量和存储量。为了提高频域声学方程的收敛速度,将超记忆混合共轭梯度法(SMHCG)引入了FDCSI。 SMHCG在超级内存梯度方法的基础上进行了改进,以适应FDCSI。 SMHCG可以加速目标函数的收敛,而无需增加任何计算和内存。 SMHCG的优势已在Marmousi模型上得到验证。

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